Apparatus and method to facilitate an iterative, machine learning based traffic classification

ABSTRACT

Aspects of the subject disclosure may include, for example, applying a model to a first unlabeled traffic item to generate a classification of the first unlabeled traffic item as a first class of traffic items or a second class of traffic items, generating a first score that is representative of a first confidence of the classification, determining that the first score is greater than a first threshold, and responsive to determining that the first score is greater than the first threshold, refining the model in accordance with at least the classification, resulting in a modified model. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to an apparatus and method to facilitatean iterative, machine learning based traffic classification.

BACKGROUND

The ability to properly classify traffic on a network is an importantaspect of maintaining and operating the network. For example, a properclassification of the traffic enables a network operator/serviceprovider (e.g., access network providers, core network providers, cloudproviders, and content delivery providers) to increase/enhance a qualityof experience (QoE)/user experience in relation to services providedover the network, while simultaneously decreasing/reducing the number ofnetwork resources that are required/needed. In this respect, an abilityto properly classify traffic on a network is an important parameter inrelation to network efficiency and scale.

As networks continue to be placed into operational service or expanded,the amount of total traffic over such networks tends to increase. Thus,it is generally impractical/inefficient to manually examine every itemof traffic that may traverse a network in order to classify each item.In this regard, a header of a traffic item may include metadatadescribing a class/category of the traffic item. This header may beexamined via, e.g., a probe or other network element, in order toidentify the metadata (and thus, the class/category of the trafficitem). However, increasingly traffic is being encrypted prior to beingtransmitted/traversing a network in view of privacy considerations andin an effort to prevent unauthorized third-parties from accessing thetraffic. Thus, network operators/service provides are unable toclassify/categorize encrypted traffic items via the use of a probe ornetwork element as set forth above.

Still further, a machine learning/deep learning (ML/DL) model-basedapproach has been suggested, whereby during a training/calibration phaselabeled/tagged traffic traverses the network. Labels/Tags of thelabeled/tagged traffic specify predefined categories/classes of traffic,such that a model is able to decipher/determine/learn patterns intraffic according to the specified category/class of the traffic. Insubsequent phases, the model predicts a category/class ofunlabeled/untagged traffic based on what the model learns during thetraining/calibration phase.

While effective in theory, the aforementioned model-based approach isineffective in classifying unlabeled/untagged traffic in practicalapplications/environments due to at least: (1) cost considerations(e.g., a use of a limited number of labeled sets/items of traffic thatare available during the training/calibration phase), and (2) theintroduction of new or enhanced traffic (which may originate from new orenhanced applications) that the model classifies with poorresults/accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 in accordance with various aspects described herein.

FIG. 2B depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for classifying network traffic in accordance with one ormore classes or categories. Other embodiments are described in thesubject disclosure.

One or more aspects of the subject disclosure include obtaining a firstplurality of traffic items, wherein each traffic item of the firstplurality of traffic items is labeled with a first label as videotraffic, obtaining a second plurality of traffic items, wherein eachtraffic item of the second plurality of traffic items is labeled with asecond label as non-video traffic, generating a model in accordance withthe first plurality of traffic items, the first label, the secondplurality of traffic items, and the second label, obtaining a firstunlabeled traffic item, wherein the first unlabeled traffic item isencrypted, applying the model to the first unlabeled traffic item togenerate a first prediction regarding whether the first unlabeledtraffic item is a video traffic item or a non-video traffic item, andgenerating a first score that is representative of a first confidence ofthe first prediction.

One or more aspects of the subject disclosure include obtaining a firstunlabeled traffic item, wherein the first unlabeled traffic item isencrypted, applying a model to the first unlabeled traffic item togenerate a first prediction regarding whether the first unlabeledtraffic item is a video traffic item or a non-video traffic item,generating a first score that is representative of a first confidence ofthe first prediction, and outputting, via a user interface, anindication of the first prediction and the first score.

One or more aspects of the subject disclosure include applying a modelto a first unlabeled traffic item to generate a classification of thefirst unlabeled traffic item as a first class of traffic items or asecond class of traffic items, generating a first score that isrepresentative of a first confidence of the classification, determiningthat the first score is greater than a first threshold, and responsiveto determining that the first score is greater than the first threshold,refining the model in accordance with at least the classification,resulting in a modified model.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a communications network 100 inaccordance with various aspects described herein. For example,communications network 100 can facilitate in whole or in part obtaininga first plurality of traffic items, wherein each traffic item of thefirst plurality of traffic items is labeled with a first label as videotraffic, obtaining a second plurality of traffic items, wherein eachtraffic item of the second plurality of traffic items is labeled with asecond label as non-video traffic, generating a model in accordance withthe first plurality of traffic items, the first label, the secondplurality of traffic items, and the second label, obtaining a firstunlabeled traffic item, wherein the first unlabeled traffic item isencrypted, applying the model to the first unlabeled traffic item togenerate a first prediction regarding whether the first unlabeledtraffic item is a video traffic item or a non-video traffic item, andgenerating a first score that is representative of a first confidence ofthe first prediction. Communications network 100 can facilitate in wholeor in part obtaining a first unlabeled traffic item, wherein the firstunlabeled traffic item is encrypted, applying a model to the firstunlabeled traffic item to generate a first prediction regarding whetherthe first unlabeled traffic item is a video traffic item or a non-videotraffic item, generating a first score that is representative of a firstconfidence of the first prediction, and outputting, via a userinterface, an indication of the first prediction and the first score.Communications network 100 can facilitate in whole or in part applying amodel to a first unlabeled traffic item to generate a classification ofthe first unlabeled traffic item as a first class of traffic items or asecond class of traffic items, generating a first score that isrepresentative of a first confidence of the classification, determiningthat the first score is greater than a first threshold, and responsiveto determining that the first score is greater than the first threshold,refining the model in accordance with at least the classification,resulting in a modified model.

In FIG. 1, a communications network 125 is presented for providingbroadband access 110 to a plurality of data terminals 114 via accessterminal 112, wireless access 120 to a plurality of mobile devices 124and vehicle 126 via base station or access point 122, voice access 130to a plurality of telephony devices 134, via switching device 132 and/ormedia access 140 to a plurality of audio/video display devices 144 viamedia terminal 142. In addition, communication network 125 is coupled toone or more content sources 175 of audio, video, graphics, text and/orother media. While broadband access 110, wireless access 120, voiceaccess 130 and media access 140 are shown separately, one or more ofthese forms of access can be combined to provide multiple accessservices to a single client device (e.g., mobile devices 124 can receivemedia content via media terminal 142, data terminal 114 can be providedvoice access via switching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system 200 a. The system 200 a may function within, orbe operatively overlaid upon, the communication network 100 of FIG. 1 inaccordance with various aspects described herein. The system 200 a maybe used to categorize/classify traffic/traffic items that is/are presentin one or more networks. As an illustrative example, the system 200 amay be used to classify an item of traffic as a video item or anon-video item. Other categories/classifications may beused/provided/obtained in some embodiments.

For the sake of illustrative convenience/ease, the use/operation of thesystem 200 a is described below in relation to FIG. 2B. FIG. 2B depictsan illustrative embodiment of a method 200 b in accordance with variousaspects described herein. One skilled in the art will appreciate, basedon a review of this disclosure, that aspects of the method 200 b may beexecuted/implemented/practiced in conjunction with systems, devices, andcomponents beyond what is shown in FIG. 2A. Similarly, the system 200 amay execute one or more methodological acts beyond what is describedbelow in relation to FIG. 2B.

In blocks 204 b and 208 b, one or more models may be trained/established(block 208 b) based on/responsive to obtaining items of labeled/taggedtraffic (block 204). As part of block 204 b, a counter (referred tohereinafter as a retry counter) may be initialized, illustratively to avalue of zero. The role/function/purpose of the retry counter willbecome clearer in the disclosure that follows.

The labeled/tagged traffic of block 204 b may identify a predefinedcategory/class of the traffic. For example, as shown in FIG. 2A a modeltraining component 202 a may utilize/obtain as inputs items oflabeled/tagged traffic 206 a-1, 206 a-2, 210 a-1, and 210 a-2. Thetraffic item 206 a-1 may correspond to a first video traffic item andthe traffic item 206 a-2 may correspond to a second video traffic item.The traffic item 210 a-1 may correspond to a first non-video trafficitem and the traffic item 210 a-2 may correspond to a second non-videotraffic item.

Video traffic items may include audiovisual aspects, whereas non-videotraffic items may, illustratively, include only audio aspects (e.g.,audio tracks such as music, books on tape, etc.), only visual aspects(e.g., still-frame images), text, etc. In some embodiments, thenon-video traffic items may adhere/conform to file transfer protocol(FTP) traffic and/or file-sharing data-based traffic. In general,traffic/traffic items may be communicated between a client and a serveron a network, between peer devices on a network, between processors of adistributed computing/processing environment, etc. The tags/labels ofone or more of the traffic items 206 a-1, 206 a-2, 210 a-1, and 210 a-2may include an identification of a source (e.g., an originator and/or adistributor) of the respective traffic items 206 a-1, 206 a-2, 210 a-1,and 210 a-2.

While four labeled traffic items 206 a-1, 206 a-2, 210 a-1, and 210 a-2are shown in FIG. 2A, in many embodiments the number/count of labeledtraffic items may be different from (e.g., larger than) four in order toproperly/sufficiently engage the model training component 202 a. As partof block 208 b, the model training component 202 a mayassess/analyze/identify patterns in the labeled traffic items (e.g., thelabeled traffic items 206 a-1, 206 a-2, 210 a-1, and 210 a-2) in orderto establish a relationship between those patterns and thecategories/classes (e.g., video or non-video) of the labeled trafficitems.

As part of block 208 b, the model training component 202 a maygenerate/establish a (working) model 214 a in the first instance.Thereafter, the working model 214 a may be used to categorize/classifyan unknown/unlabeled traffic item (e.g., unlabeled traffic item 218 a)as described further below. Following the establishment of the workingmodel 214 a in the first instance, the working model 214 a may beupdated/modified/refined as described further below.

In block 212 b, the unlabeled traffic item 218 a may be obtained. Forexample, the unlabeled traffic item 218 a may be obtained based on theunlabeled traffic item 218 a being transmitted from one or more sourcesvia one or more networks/network elements/network infrastructure. Theunlabeled traffic item 218 a may be encrypted.

In block 216 b, the model 214 a may be applied to the unlabeled trafficitem 218 a. Assuming that the unlabeled traffic item 218 a is encrypted,the model 214 a may be applied to the unlabeled traffic item 218 a aspart of block 216 b without decrypting the unlabeled traffic item 218.As part of block 216 b, the model 214 a (or another element/component)may identify parameters of the unlabeled traffic item 218 a. Theparameters may include: the number of bytes transmitted or received,potentially as a function of time (e.g., 10 bytes per timeslot in afirst period of time, 25 bytes per timeslot in a second period of time,etc.), one or more server name indication (SNI) identifiers and/ordomain name identifiers, a summary of (data of the) unlabeled trafficitem 218 a in terms of throughput and/or duration, etc.

In block 220 b, and in response to the identification of the parametersin block 216 b, the model 214 a may generate a prediction 222 a in termsof a category/classification of the unlabeled traffic item 218 a. Forexample, as part of block 220 b, the parameters of block 216 b may becompared to the patterns identified in block 208 b to select acategory/class for the unlabeled traffic item 218 a. In an exemplaryembodiment, the unlabeled traffic item 218 a may be classified as avideo item or a non-video item as part of block 220 b.

In block 224 b, one or more scores may be generated by, e.g., the model214 a, in terms of the predicted category/class of block 220 b. A scoregenerated in block 224 b may be representative of a confidence of themodel 214 a in terms of the prediction of the category/class in block220 b, and may be based on the comparison set forth above with respectto block 220 b. For example, if the comparison of block 220 b yields anexact or substantial match (e.g., a match that is greater than athreshold), confidence in the prediction of the category/class of theunlabeled traffic item 218 a as part of block 220 b may be ‘high’,whereas if the comparison of block 220 b yields a poor match (e.g., amatch that is less than a threshold), confidence in the prediction ofthe category/class of the unlabeled traffic item 218 a as part of block220 b may be ‘low’.

In block 228 b, a determination may be made whether the score/confidenceof block 224 b (and/or block 244 b as described in further detail below)is greater than a first threshold. If so (e.g., the “yes” path is takenfrom block 228 b), flow may proceed from block 228 b to block 208 b.Otherwise (e.g., the “no” path is taken from block 228 b), flow mayproceed from block 228 b to block 232 b.

As part of the flow from block 228 b to block 208 b, the unlabeledtraffic 218 a (of block 212 b) may be treated similar to the labeledtraffic (of block 204 b) in terms of an update/refinement/modificationof the model 214 a via the model training component 202 a. Statedslightly differently, as part of the flow from block 228 b to block 208b, the unlabeled traffic 218 a may effectively serve as another input(in addition to the labeled traffic items 206 a-1, 206 a-2, 210 a-1, and210 a-2) to the model training component 202 a, such that the model 214a generated/produced by the model training component 202 a may berefined/modified/updated in accordance with the unlabeled traffic 218 a.In this manner, the model 214 a may be updated in accordance withtraffic that has an associated ‘high’ degree ofcategorization/classification tied to it. In other words, the model 214a may be a ‘living’ model that is adapted over time as new/additionalunlabeled traffic/traffic items is/are obtained. As the model 214 a isrefined over time, any error in terms of the prediction of theclassification/categorization block 220 b will tend to converge to zero;e.g., the model 214 a will become more accurate the more the model 214 ais used/exercised.

As part of the flow from block 228 b to block 208 b, thescore/confidence of block 224 b (or block 244 b as described furtherbelow) may serve as a weight in terms of the refinement of the modelthat is obtained. For example, and assuming a score range from 1 to 100(where 100 represents absolute certainty, e.g., the highest degree ofconfidence, and 1 represents the least degree of confidence), thelabeled traffic of block 204 b may be assigned a weight of 100. Thus,the extent or degree to which the model 214 a is refined may be afunction of the degree of confidence associated with an unlabeledtraffic item. Unlabeled traffic items that have a high degree ofconfidence associated with them may more directly influence/impact themodel than those unlabeled traffic items having a low degree ofconfidence associated with them.

In block 232 b, a determination may be made whether the score/confidenceof block 224 b (and/or block 244 b as described in further detail below)is greater than a second threshold (where the second threshold of block232 b is less than the first threshold of block 228 b). If so (e.g., the“yes” path is taken out of block 232 b), flow may proceed from block 232b to block 236 b. Otherwise (e.g., the no” path is taken from block 232b), flow may proceed from block 232 b to block 240 b.

The flow from block 232 b to block 240 b may signify that theconfidence/score is “so low” so as to not warrant additionalanalysis/use of computing resources. In other words, unlike the scenariodescribed above in respect of the flow from block 228 b to block 208 b(wherein the unlabeled traffic 218 a is used to update/modify/refine themodel 214 a due to the high degree of confidence associated with theprediction of the categorization/classification in block 220 b), theflow from block 232 b to block 240 b may serve to ensure the integrityof the model 214 a by ensuring that low-confidence predictions of thecategorizations/classifications of block 220 b do notinfluence/negatively impact the model 214 a.

In block 236 b, a determination may be made regarding whether the retrycounter has a value that is greater than an associated threshold(referred to herein as a retry threshold). If so (e.g., the “yes” pathis taken out of block 236 b), flow may proceed from block 236 b to block240 b. Otherwise (e.g., the no” path is taken from block 236 b), flowmay proceed from block 236 b to block 244 b.

The flow from block 236 b to block 240 b may be representative of atrade-off between attempting to incorporate the unlabeled traffic 218 a(having an associated medium-valued confidence/score [e.g., aconfidence/score between the first threshold of block 228 b and thesecond threshold of block 232 b] as generated in block 224 b) as part ofthe model 214 a on the one hand versus preserving resources and theintegrity of the model 214 a on the other hand. Based on a givenapplication/environment at hand, the value of the retry threshold ofblock 236 b (and/or the second threshold of block 232 b) may beselected, accordingly, to facilitate an appropriate trade-off.

In block 244 b, the testing component 226 a may perform additionaltesting/verification on the unlabeled traffic 218 a to enhance theconfidence/score of block 224 b (or the confidence/score generated aspart of a prior execution of block 236 b with respect to the unlabeledtraffic 218 a obtained in block 212 b). For example, as part of block244 b the unlabeled traffic 218 a may be subject to additionalalgorithms, surveys/polling of different entities or computing devices,etc., in an effort to gain confidence as to whether the prediction ofblock 220 b is indeed accurate. As part of block 244 b, the retrycounter may be incremented.

From block 244 b, flow may proceed to block 228 b. As part of that flowfrom block 244 b to block 228 b, the enhanced confidence obtained aspart of block 244 b may be assessed relative to the first threshold.Stated slightly differently, following the first execution of blocks 228b and 232 b with respect to the unlabeled traffic 218 a (of block 212b), subsequent executions of the block 228 b and the block 232 b mayutilize the enhanced confidence obtained as part of block 244 b (in lieuof the confidence generated in block 224 b).

In block 240 b, the method 200 b may end/terminate with respect to theunlabeled traffic 218 a of block 212 b. As described above, aspects ofthe block 240 b may serve to exclude the unlabeled traffic 218 a frominfluencing/impacting (a refinement of) the model 214 a.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2B, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Aspects of the method 200 b may be executed iteratively or repeatedly.For example, the method 200 b may be executed each time a new oradditional traffic item is obtained.

As described herein, traffic that traverses a network may take one ormore forms. For example, a traffic item may include control data, apayload/payload data, etc. A traffic item may pertain/relate to one ormore applications, programs, systems, devices, components, etc. Atraffic item may be encrypted or unencrypted in traversing at least partof a network infrastructure from a source to a destination.

As described herein, a model may be generated in a first instance inaccordance with known/labeled categories/classes of traffic. The modelmay subsequently be refined/modified/updated, in accordance with amachine-learning based approach, in view of additional/new traffic thatis obtained. In some embodiments, the model may be used to generate aprediction regarding a category/class associated with an unlabeledtraffic item, and a confidence associated with the prediction. If theconfidence is sufficiently high, the prediction may be used to drive anupdate to the model as warranted.

In accordance with aspects of this disclosure, acategorization/classification assigned to unlabeled traffic may serve asa parameter/input to a decision-making process regarding autilization/allocation of one or more resources. For example, a networkoperator/service provider may select a first resource with a firstparameter (e.g., a first transmission bandwidth) for a firstcategory/class of traffic (e.g., video traffic) and may select a secondresource with a second parameter (e.g., a second transmission bandwidth)for a second category/class of traffic (e.g., non-video traffic). Moregenerally, the assignment of a category/class to unlabeled traffic mayfacilitate efficiency and scalability with respect to the operation andmaintenance of one or more networks.

An assignment/allocation of a resource may include a selection of anoperating parameter for the resource. A category/classification oftraffic may serve as one of a plurality of factors/considerations interms of an assignment/allocation of a resource. For example, otherfactors/considerations may include a volume/amount of the traffic,forecasted traffic, an identification of a date or time, anidentification of a type (e.g., make or model) of device that isgenerating/transmitting and/or receiving traffic, a subscription plan orlicense agreement/arrangement in terms of, e.g., a quality of serviceparameter, etc.

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of communicationnetwork 100, the subsystems and functions of system 200 a, and method200 b presented in FIGS. 1, 2A, and 2B. For example, virtualizedcommunication network 300 can facilitate in whole or in part obtaining afirst plurality of traffic items, wherein each traffic item of the firstplurality of traffic items is labeled with a first label as videotraffic, obtaining a second plurality of traffic items, wherein eachtraffic item of the second plurality of traffic items is labeled with asecond label as non-video traffic, generating a model in accordance withthe first plurality of traffic items, the first label, the secondplurality of traffic items, and the second label, obtaining a firstunlabeled traffic item, wherein the first unlabeled traffic item isencrypted, applying the model to the first unlabeled traffic item togenerate a first prediction regarding whether the first unlabeledtraffic item is a video traffic item or a non-video traffic item, andgenerating a first score that is representative of a first confidence ofthe first prediction. Communication network 300 can facilitate in wholeor in part obtaining a first unlabeled traffic item, wherein the firstunlabeled traffic item is encrypted, applying a model to the firstunlabeled traffic item to generate a first prediction regarding whetherthe first unlabeled traffic item is a video traffic item or a non-videotraffic item, generating a first score that is representative of a firstconfidence of the first prediction, and outputting, via a userinterface, an indication of the first prediction and the first score.Communication network 300 can facilitate in whole or in part applying amodel to a first unlabeled traffic item to generate a classification ofthe first unlabeled traffic item as a first class of traffic items or asecond class of traffic items, generating a first score that isrepresentative of a first confidence of the classification, determiningthat the first score is greater than a first threshold, and responsiveto determining that the first score is greater than the first threshold,refining the model in accordance with at least the classification,resulting in a modified model

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part obtaining a first plurality of trafficitems, wherein each traffic item of the first plurality of traffic itemsis labeled with a first label as video traffic, obtaining a secondplurality of traffic items, wherein each traffic item of the secondplurality of traffic items is labeled with a second label as non-videotraffic, generating a model in accordance with the first plurality oftraffic items, the first label, the second plurality of traffic items,and the second label, obtaining a first unlabeled traffic item, whereinthe first unlabeled traffic item is encrypted, applying the model to thefirst unlabeled traffic item to generate a first prediction regardingwhether the first unlabeled traffic item is a video traffic item or anon-video traffic item, and generating a first score that isrepresentative of a first confidence of the first prediction. Computingenvironment 400 can facilitate in whole or in part obtaining a firstunlabeled traffic item, wherein the first unlabeled traffic item isencrypted, applying a model to the first unlabeled traffic item togenerate a first prediction regarding whether the first unlabeledtraffic item is a video traffic item or a non-video traffic item,generating a first score that is representative of a first confidence ofthe first prediction, and outputting, via a user interface, anindication of the first prediction and the first score. Computingenvironment 400 can facilitate in whole or in part applying a model to afirst unlabeled traffic item to generate a classification of the firstunlabeled traffic item as a first class of traffic items or a secondclass of traffic items, generating a first score that is representativeof a first confidence of the classification, determining that the firstscore is greater than a first threshold, and responsive to determiningthat the first score is greater than the first threshold, refining themodel in accordance with at least the classification, resulting in amodified model.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part obtaining a first plurality of traffic items,wherein each traffic item of the first plurality of traffic items islabeled with a first label as video traffic, obtaining a secondplurality of traffic items, wherein each traffic item of the secondplurality of traffic items is labeled with a second label as non-videotraffic, generating a model in accordance with the first plurality oftraffic items, the first label, the second plurality of traffic items,and the second label, obtaining a first unlabeled traffic item, whereinthe first unlabeled traffic item is encrypted, applying the model to thefirst unlabeled traffic item to generate a first prediction regardingwhether the first unlabeled traffic item is a video traffic item or anon-video traffic item, and generating a first score that isrepresentative of a first confidence of the first prediction. Platform510 can facilitate in whole or in part obtaining a first unlabeledtraffic item, wherein the first unlabeled traffic item is encrypted,applying a model to the first unlabeled traffic item to generate a firstprediction regarding whether the first unlabeled traffic item is a videotraffic item or a non-video traffic item, generating a first score thatis representative of a first confidence of the first prediction, andoutputting, via a user interface, an indication of the first predictionand the first score. Platform 510 can facilitate in whole or in partapplying a model to a first unlabeled traffic item to generate aclassification of the first unlabeled traffic item as a first class oftraffic items or a second class of traffic items, generating a firstscore that is representative of a first confidence of theclassification, determining that the first score is greater than a firstthreshold, and responsive to determining that the first score is greaterthan the first threshold, refining the model in accordance with at leastthe classification, resulting in a modified model.

In one or more embodiments, the mobile network platform 510 can generateand receive signals transmitted and received by base stations or accesspoints such as base station or access point 122. Generally, mobilenetwork platform 510 can comprise components, e.g., nodes, gateways,interfaces, servers, or disparate platforms, that facilitate bothpacket-switched (PS) (e.g., internet protocol (IP), frame relay,asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic(e.g., voice and data), as well as control generation for networkedwireless telecommunication. As a non-limiting example, mobile networkplatform 510 can be included in telecommunications carrier networks, andcan be considered carrier-side components as discussed elsewhere herein.Mobile network platform 510 comprises CS gateway node(s) 512 which caninterface CS traffic received from legacy networks like telephonynetwork(s) 540 (e.g., public switched telephone network (PSTN), orpublic land mobile network (PLMN)) or a signaling system #7 (SS7)network 560. CS gateway node(s) 512 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway node(s) 512 can access mobility, or roaming, data generatedthrough SS7 network 560; for instance, mobility data stored in a visitedlocation register (VLR), which can reside in memory 530. Moreover, CSgateway node(s) 512 interfaces CS-based traffic and signaling and PSgateway node(s) 518. As an example, in a 3GPP UMTS network, CS gatewaynode(s) 512 can be realized at least in part in gateway GPRS supportnode(s) (GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 512, PS gateway node(s) 518, and servingnode(s) 516, is provided and dictated by radio technology(ies) utilizedby mobile network platform 510 for telecommunication over a radio accessnetwork 520 with other devices, such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It is should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part obtaining afirst plurality of traffic items, wherein each traffic item of the firstplurality of traffic items is labeled with a first label as videotraffic, obtaining a second plurality of traffic items, wherein eachtraffic item of the second plurality of traffic items is labeled with asecond label as non-video traffic, generating a model in accordance withthe first plurality of traffic items, the first label, the secondplurality of traffic items, and the second label, obtaining a firstunlabeled traffic item, wherein the first unlabeled traffic item isencrypted, applying the model to the first unlabeled traffic item togenerate a first prediction regarding whether the first unlabeledtraffic item is a video traffic item or a non-video traffic item, andgenerating a first score that is representative of a first confidence ofthe first prediction. Computing device 600 can facilitate in whole or inpart obtaining a first unlabeled traffic item, wherein the firstunlabeled traffic item is encrypted, applying a model to the firstunlabeled traffic item to generate a first prediction regarding whetherthe first unlabeled traffic item is a video traffic item or a non-videotraffic item, generating a first score that is representative of a firstconfidence of the first prediction, and outputting, via a userinterface, an indication of the first prediction and the first score.Computing device 600 can facilitate in whole or in part applying a modelto a first unlabeled traffic item to generate a classification of thefirst unlabeled traffic item as a first class of traffic items or asecond class of traffic items, generating a first score that isrepresentative of a first confidence of the classification, determiningthat the first score is greater than a first threshold, and responsiveto determining that the first score is greater than the first threshold,refining the model in accordance with at least the classification,resulting in a modified model.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, . . . ,xn), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: obtaining a first plurality oftraffic items, wherein each traffic item of the first plurality oftraffic items is labeled with a first label as video traffic; obtaininga second plurality of traffic items, wherein each traffic item of thesecond plurality of traffic items is labeled with a second label asnon-video traffic; generating a model in accordance with the firstplurality of traffic items, the first label, the second plurality oftraffic items, and the second label; obtaining a first unlabeled trafficitem, wherein the first unlabeled traffic item is encrypted; applyingthe model to the first unlabeled traffic item, without decrypting thefirst unlabeled traffic item, to generate a first prediction regardingwhether the first unlabeled traffic item is a video traffic item or anon-video traffic item; generating a first score that is representativeof a first confidence of the first prediction; determining that thefirst score is less than a first threshold and greater than a secondthreshold; and responsive to the determining that the first score isless than the first threshold and greater than the second threshold,performing a first test on the first unlabeled traffic item, wherein theperforming of the first test on the first unlabeled traffic itemenhances the first score, resulting in a first enhanced first score. 2.The device of claim 1, wherein the operations further comprise:allocating a resource of a communications network in accordance with thefirst prediction and the first score.
 3. The device of claim 2, whereinthe allocating of the resource of the communications network comprisesselecting an operating parameter of the resource.
 4. The device of claim1, wherein the operations further comprise: determining that the firstenhanced first score is greater than a third threshold; and responsiveto the determining that the first enhanced first score is greater thanthe third threshold, refining the model in accordance with at least thefirst prediction to generate a modified model.
 5. The device of claim 4,wherein the operations further comprise: obtaining a second unlabeledtraffic item, wherein the second unlabeled traffic item is encrypted;and applying the modified model to the second unlabeled traffic item togenerate a second prediction regarding whether the second unlabeledtraffic item is a video traffic item or a non-video traffic item.
 6. Thedevice of claim 5, wherein the operations further comprise: generating asecond score that is representative of a second confidence of the secondprediction; and outputting, via a user interface, an indication of thefirst prediction, the first score, the second prediction, the secondscore, or a combination thereof.
 7. The device of claim 1, wherein theoperations further comprise: determining that the first enhanced firstscore is greater than the first threshold; and responsive to thedetermining that the first enhanced first score is greater than thefirst threshold, refining the model in accordance with at least thefirst prediction to generate a modified model.
 8. The device of claim 1,wherein the operations further comprise: responsive to the performing ofthe first test, incrementing a value of a counter.
 9. The device ofclaim 8, wherein the operations further comprise: determining that thefirst enhanced first score is less than the first threshold and greaterthan the second threshold; responsive to the determining that the firstenhanced first score is less than the first threshold and greater thanthe second threshold, determining that the value of the counter is lessthan a third threshold; and responsive to the determining that the valueof the counter is less than the third threshold, performing a secondtest on the first unlabeled traffic item, wherein the performing of thesecond test on the first unlabeled traffic item enhances the firstenhanced first score, resulting in a second enhanced first score. 10.The device of claim 9, wherein the operations further comprise:determining that the second enhanced first score is greater than thefirst threshold; and responsive to the determining that the secondenhanced first score is greater than the first threshold, refining themodel in accordance with at least the first prediction to generate amodified model.
 11. The device of claim 8, wherein the operationsfurther comprise: determining that the first enhanced first score isless than the first threshold and greater than the second threshold;responsive to the determining that the first enhanced first score isless than the first threshold and greater than the second threshold,determining that the value of the counter is greater than a thirdthreshold; and responsive to the determining that the value of thecounter is greater than the third threshold, excluding the firstunlabeled traffic item from a refinement of the model.
 12. Anon-transitory machine-readable medium, comprising executableinstructions that, when executed by a processing system including aprocessor, facilitate performance of operations, the operationscomprising: obtaining a first unlabeled traffic item, wherein the firstunlabeled traffic item is encrypted; applying a model to the firstunlabeled traffic item, without decrypting the first unlabeled trafficitem, to generate a first prediction regarding whether the firstunlabeled traffic item is a video traffic item or a non-video trafficitem; generating a first score that is representative of a firstconfidence of the first prediction; outputting, via a user interface, anindication of the first prediction and the first score; determining thatthe first score is less than a first threshold and greater than a secondthreshold; and responsive to the determining that the first score isless than the first threshold and greater than the second threshold,performing a first test on the first unlabeled traffic item, wherein theperforming of the first test on the first unlabeled traffic itemenhances the first score, resulting in a first enhanced first score. 13.The non-transitory machine-readable medium of claim 12, wherein theapplying of the model to the first unlabeled traffic item results in anidentification of at least one parameter of the first unlabeled trafficitem, and wherein the first prediction is based on the identification ofthe at least one parameter.
 14. The non-transitory machine-readablemedium of claim 13, wherein the at least one parameter comprises aplurality of parameters, and wherein the plurality of parametersincludes at least two of: a number of bytes transmitted or received as afunction of time; a server name indication (SNI) identifier, a domainname identifier, or a combination thereof; and a summary of data of thefirst unlabeled traffic item in terms of throughput, duration, or acombination thereof.
 15. The non-transitory machine-readable medium ofclaim 12, wherein the operations further comprise: generating the modelin accordance with a plurality of labels, wherein each label of theplurality of labels is associated with a respective labeled traffic itemincluded in a plurality of labeled traffic items.
 16. The non-transitorymachine-readable medium of claim 15, wherein the generating of the modelcomprises establishing a relationship between each label and patterns inthe respective labeled traffic item.
 17. The non-transitorymachine-readable medium of claim 12, wherein the operations furthercomprise: refining the model in accordance with the first prediction andthe first score, resulting in a modified model; obtaining a secondunlabeled traffic item; and applying the modified model to the secondunlabeled traffic item to generate a second prediction regarding whetherthe second unlabeled traffic item is a video traffic item or a non-videotraffic item.
 18. A method, comprising: applying, by a processing systemincluding a processor, a model to a first unlabeled traffic item togenerate a classification of the first unlabeled traffic item as a firstclass of traffic items or a second class of traffic items; generating,by the processing system, a first score that is representative of afirst confidence of the classification; determining, by the processingsystem, that the first score is less than a first threshold and greaterthan a second threshold; and responsive to the determining, performing afirst test on the first unlabeled traffic item, wherein the performingof the first test on the first unlabeled traffic item enhances the firstscore, resulting in a first enhanced first score.
 19. The method ofclaim 18, wherein the first unlabeled traffic item is encrypted, andwherein the applying of the model to the first unlabeled traffic item isperformed without decrypting the first unlabeled traffic item.
 20. Themethod of claim 19, wherein the first class corresponds to a videotraffic item and the second class corresponds to a non-video trafficitem.